Life cycle assessment of an integrated forest biorefinery: hot water extraction process case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The environmental footprint of bioproducts depends on the performance and implementation strategy of the biorefinery processes through which they are produced. Life cycle assessment ( LCA ) studies are categorized into two general types: attributional and consequential. The consequential life cycle assessment ( CLCA ) method illustrates the change of flows to and from environment, resulting from different potential decisions. Depending on the analysis goal, CLCA is known to be the proper approach to address the environmental analysis of integrated biorefineries with multiple bioproducts. In this study, an LCA of hot water extraction‐based biorefinery strategy was performed, including five production pathways. Defined process options consisted of an extraction of hemicellulose to produce (i) biogas, (ii) hemicellulose for animal feed, (iii) hemicellulose for C5 ‐sugars, (iv) C5 ‐sugars, and (v) furfural. Except for the “biogas”, acetate salt was the by‐product of all the process options. Consequential LCA results proved that the bark consumption, chemicals, and bioproducts transportation have significant environmental impacts. ‘Hemicellulose for C5 ‐sugars’ and ‘ C5 ‐sugars’ outperformed other alternative options with a greenhouse gas reduction of 80% and 68%, respectively. Also, normalized results of these two options presented remarkable improvement of more than three times in human health impacts in comparison to existing process at the case study mill. © 2015 Society of Chemical Industry and John Wiley & Sons, Ltd
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it